Methods and apparatus to capture images

ABSTRACT

Methods and apparatus to operate a mobile phone are disclosed. An example mobile phone disclosed herein includes an illumination source and an image sensor to capture a first image of a face. The disclosed example mobile phone also includes a logic circuit to determine whether the face in the first image is in a first orientation with respect to the image sensor. When the face in the first image is in the first orientation, the logic circuit is to trigger the image sensor to capture a second image and perform a facial recognition process on the second image, but not on the first image.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 15/419,120, filed Jan. 30, 2017, now U.S. Pat. No. 9,843,717, whicharises from a continuation of U.S. patent application Ser. No.14/732,175, filed Jun. 5, 2015, now U.S. Pat. No. 9,560,267, whicharises from a continuation of U.S. patent application Ser. No.13/327,227, filed Dec. 15, 2011, now U.S. Pat. No. 9,082,004. U.S.patent application Ser. No. 15/419,120, U.S. patent application Ser. No.14/732,175 and U.S. patent application Ser. No. 13/327,227 are herebyincorporated herein by reference in their entirety. Priority to U.S.patent application Ser. No. 15/419,120, U.S. patent application Ser. No.14/732,175 and U.S. patent application Ser. No. 13/327,227 is claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement and, moreparticularly, to methods and apparatus to capture images.

BACKGROUND

Audience measurement of media content (e.g., broadcast television and/orradio, stored audio and/or video content played back from a memory suchas a digital video recorder or a digital video disc, audio and/or videocontent played via the Internet, video games, etc.) often involvescollection of content identifying data (e.g., signature(s),fingerprint(s), embedded code(s), channel information, time ofconsumption information, etc.) and people data (e.g., identifiers,demographic data associated with audience members, etc.). The contentidentifying data and the people data can be combined to generate, forexample, media exposure data indicative of amount(s) and/or type(s) ofpeople that were exposed to specific piece(s) of media content.

In some audience measurement systems, the collected people data includesan amount of people being exposed to media content. To calculate theamount of people being exposed to the media content, some measurementsystems capture a series of images of a media exposure environment(e.g., a television room, a family room, a living room, a bar, arestaurant, etc.) and analyze the images to determine how many peopleappear in the images at a particular date and time. The calculatedamount of people in the media exposure environment can be correlatedwith media content being presented at the particular date and time toprovide exposure data (e.g., ratings data) for that media content.Additionally, some audience measurement systems identify people in theimages via one or more identification techniques such as, for example,facial recognition.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an example exposure environment includingan example audience measurement device disclosed herein.

FIG. 2 is a block diagram of an example implementation of the exampleaudience measurement device of FIG. 1.

FIG. 3 is a block diagram of an example implementation of the exampleimage sensor of FIG. 2.

FIG. 4 is a flowchart illustrating example machine readable instructionsthat may be executed to implement the example image sensor of FIGS. 1, 2and/or 3.

FIG. 5 is a block diagram of an example processing system capable ofexecuting the example machine readable instructions of FIG. 4 toimplement the example image sensor of FIGS. 1, 2 and/or 3.

DETAILED DESCRIPTION

To count people in a media exposure environment, such as a room of ahouse in which a television is located, some audience measurementsystems attempt to recognize objects as humans in a series of capturedimages of the room. A tally is maintained for each frame of image datato reflect an amount of people in the room at a time corresponding to arespective frame. That is, each recognition of an object as a human in aframe increases the tally associated with that frame. The audiencemeasurement system counting the people in the room may also collectcontent identifying information to identify media content beingpresented (e.g., aurally and/or visually) in the room. With theidentification of the media content and the amount of people in the roomat a given date and time, the audience measurement system is aware ofhow many people were exposed to the specific media content.

Additionally, some systems recognize an identity of one or more of thedetected humans by, for example, performing a facial recognition processon image data of one or more of the frames, receiving identifier(s) fromthe detected humans, detecting identifying signal(s) generated bydevices carried by the humans, etc. Personal identification informationcan be used in conjunction with the content identifying informationand/or the tally information to generate exposure information related tothe content. When an audience measurement system uses a facialrecognition process to identify people, an accuracy of theidentification increases with an increase in resolution of the imagedata on which the facial recognition process is performed. In otherwords, the higher the resolution of a frame of image data, the morelikely identification made via facial recognition will be accurate.

To provide high-resolution image data, audience measurement systems thatinclude facial recognition capabilities typically employ high-resolutionimage sensors equipped with an illumination source, such as an infrared(IR) light emitting diode (LED). In previous systems, each time thehigh-resolution image sensor captures a frame of image data, theillumination source illuminates a surrounding area. The resultingillumination provides lighting conditions favorable to capturinghigh-resolution image data. When the illumination source is an IR LED,the illumination source emits IR light to enable the image sensor tocapture illuminated objects. In addition to the IR light, IR emittersalso emit light from the visible spectrum that appears as a red glowfrom the emitter.

Frequent activation of the illumination sources when capturing imagedata represents a significant power drain for the audience measurementsystem. Moreover, frequent interval activation (e.g., every two seconds)of the illumination source shortens a lifetime of the illuminationsource. Additionally, due to significant amounts of heat generated bythe illumination sources, heat sinking devices and techniques aretypically needed in systems that activate the illumination sourcesfrequently. Further, light emissions of the illumination source has thepotential to annoy people such as, for example, members of a panel thatare exposed to the illumination source while in the presence of theaudience measurement system. In an audience measurement system utilizingan IR LED, the red glow emitted from the illumination source is ablinking red light that faces the panel members while the panel membersare, for example, watching a television. This blinking red light mayannoy some panel members. Annoyance of panel members is undesirable andmay prove detrimental to an ability of the audience measurement systemto maintain persons in the panel and/or to collect as much data aspossible. That is, some audience measurement systems rely on the willingparticipation of panel members and, thus, reduction or elimination ofannoying aspects of the system is beneficial to avoid impairingwillingness to volunteer. An annoying feature of the audiencemeasurement system may decrease panelist compliance with and/orparticipation in the system.

Example methods, apparatus, and articles of manufacture disclosed hereinreserve use of an illumination source of a high-resolution image sensorfor frames of image data designated for processing that requireshigh-resolution image data. Example frames designated for suchprocessing include frames on which a facial recognition process is to beexecuted. For frames not designated for processing that requireshigh-resolution image data, example methods, apparatus, and articles ofmanufacture disclosed herein capture image data without the use of theillumination source. Example frames not designated for processing thatrequires high-resolution image data include frames on which a personcount (e.g., a body count) is to be executed without recognizing anidentity of the detected persons. Additionally, when image data iscaptured without activation of the illumination source (e.g., when thecorresponding frame will not be subjected to facial recognition),example methods, apparatus, and articles of manufacture disclosed hereinenhance resulting images to compensate for low light levels and loss ofcontrast due to lack of illumination. In particular, example methods,apparatus, and articles of manufacture disclosed herein employ a pixelbinning procedure on the image data captured without use of theillumination source. Binning is the process of summing pixel values in aneighborhood of pixels (e.g., a 2×2, 3×3, 4×4, etc. area), therebycapturing more light per pixel at the cost of lower resolution. However,for example methods, apparatus, and articles of manufacture disclosedherein, the lower resolution is acceptable for the frames capturedwithout the use of the illumination source because, as described above,those frames will not be subjected to processing that requireshigh-resolution data.

Accordingly, example methods, apparatus, and articles of manufacturedisclosed herein selectively activate an illumination source associatedwith an image sensor for certain frames. In contrast, previous systemsactivate the illumination source for each frame captured by acorresponding image sensor. In many instances, frames not designated forhigh-resolution processing (e.g., frames used solely to count people)greatly outnumber the frames designated for high-resolution frame (e.g.,frame used for facial recognition). In fact, as described below, a modeof an image sensor during which image data is captured without the useof the illumination source (e.g., because high-resolution image data isnot necessary for the corresponding frame(s)) is referred to herein as a‘majority capture’ mode. Conversely, because the number of framesrequiring high-resolution image data is far less than the number offrames for which lower resolution image data is acceptable, a mode ofthe image sensor during which image data is captured with the use of theillumination source is referred to herein as a ‘minority capture’ mode.

Because the illumination source is a significant power consumer,selective activation of the illumination source provided by examplemethods, apparatus, and articles of manufacture disclosed herein greatlyreduce power consumption levels of the image sensors of audiencemeasurement systems. Moreover, the selective activation provided byexample methods, apparatus, and articles of manufacture disclosed hereinextends the operational lifetime of the image sensors of audiencemeasurement systems by less frequently operating the correspondingillumination sources. Further, the selective activation provided byexample methods, apparatus, and articles of manufacture disclosed hereinreduces or even eliminates the need for heat sinking devices and/ortechniques otherwise required to dissipate heat generated by theillumination sources. In addition to the reduced resource consumptionprovided by example methods, apparatus, and articles of manufacturedisclosed herein, audience measurement methods, systems, and articles ofmanufacture employing the selective activation disclosed herein reducethe likelihood that panel members become irritated by light and/or glowemitted from illumination sources. As described above, illuminationsources of image sensors typically face panel members in a mediaexposure environment (e.g., a television room) and, thus, the panelmembers are subjected to a blinking and/or glowing light whenever theillumination source is activated. In previous systems utilizing ahigh-resolution image sensor, each frame is captured with the use of theillumination source, resulting in a blinking light (e.g., red light inthe case of an IR LED flash unit being used as the illumination source)or a light that is seemingly persistently on throughout operation of thesystem. Selectively activating the illumination source for framesrequiring high-resolution image data and not activating the illuminationsource for other frames, as disclosed herein, considerably reduces theinstances of illumination of the media exposure environment and, thus,the potential for irritating the panel members.

FIG. 1 is an illustration of an example media exposure environment 100including a media presentation device 102 and an example audiencemeasurement device 104 for measuring and/or identifying an audience 106of the media presentation device 102. In the illustrated example of FIG.1, the media exposure environment 100 is a room of a household that hasbeen statistically selected to develop television ratings data for apopulation/demographic of interest. Assumingly, one or more persons ofthe household have registered with the system and provided thisdemographic information. The example audience measurement device 104 canbe implemented in additional and/or alternative types of environmentssuch as, for example, a room in a non-statistically selected household,a theater, a restaurant, a tavern, a retail location, an arena, etc. Inthe illustrated example of FIG. 1, the media presentation device is atelevision 102 coupled to a set-top box (STB) 108 that implements adigital video recorder (DVR) and a digital versatile disc (DVD) player.The example audience measurement device 104 can be implemented inconnection with additional and/or alternative types of mediapresentation devices such as, for example, a radio, a computer monitor,a video game console and/or any other communication device able topresent content to one or more individuals.

The example audience measurement device 104 of FIG. 1 utilizes a camera110 to capture a plurality of time stamped frames of image data of theenvironment 100. The example camera 110 captures images within a fieldof view defined by the dotted lines of FIG. 1. In the example shown inFIG. 1, an image captured by the camera 110 includes each member of athree-person audience 106. The images captured by the camera 110 areused to generate people tallies representative of how many people are inthe audience 106 and/or personal identifications of the people in theaudience. As described in detail below, the example audience measurementdevice 104 of FIG. 1 also monitors the environment 100 to identify mediacontent being presented (e.g., displayed, played, etc.) by thetelevision 102 and/or other media presentation devices to which theaudience 106 is exposed. Identification(s) of media content to which theaudience 106 is exposed are correlated with the people tallies and/orthe personal identifications to generate exposure data for the mediacontent.

FIG. 2 is a block diagram of an example implementation of the exampleaudience measurement device 104 of FIG. 1. The example audiencemeasurement device 104 of FIG. 2 includes an audience detector 200 and acontent identifier 202. The example audience detector 200 includes animage sensor 204, a people counter 206, a person identifier 207, a timestamper 208, and a memory 210. The example image sensor 204 of FIG. 2captures frames of image data of the environment 100, which includes theaudience 106 being exposed to a presentation output by the mediapresentation device 102 of FIG. 1. In the illustrated example, the imagesensor 204 is implemented by a high-resolution camera capable ofcapturing high-resolution image data, such as an image sensor configuredto capture images at a resolution of, for example, 1920×1080 or1280×960. In some examples, the image sensor 204 and/or the camera 110of FIG. 1 is implemented by a gaming system, such as XBOX® Kinect®.

In the illustrated example, the frames obtained by the image sensor 204of FIG. 2 are conveyed to the people counter 206. The example peoplecounter 206 determines how many people appear in each of the receivedframes and records each of the amounts of people as a tally for eachframe. The example people counter 206 can determine how many peopleappear in a frame in any suitable manner using any suitable technique.For example, the people counter 206 of FIG. 2 recognizes a general shapeof a human body and/or a human body part, such as a head and/or torso.Additionally or alternatively, the example people counter 206 of FIG. 2may count a number of “blobs” that appear in the frame and count eachdistinct blob as a person. Recognizing human shapes and counting “blobs”are illustrative examples and the people counter 206 of FIG. 2 can countpeople using any number of additional and/or alternative techniques. Anexample manner of counting people is described by Blumenthal in U.S.patent application Ser. No. 10/538,483, filed on Jun. 8, 2005, now U.S.Pat. No. 7,203,338, which is hereby incorporated herein by reference inits entirety. To track the number of detected people in a room, theexample people counter 206 of FIG. 2 also tracks a position (e.g., anX-Y coordinate) of each detected person.

In the illustrated example, some frames obtained by the image sensor 204of FIG. 2 are conveyed to the person identifier 207 in addition to or inlieu of the people counter 206. As described in greater detail below inconnection with FIG. 3, the frames conveyed to the example personidentifier 207 of FIG. 2 are frames designated (e.g., by the imagesensor 204 and/or any other component of the audience measurement system100) for a facial recognition procedure such that people captured inthose frames can be individually identified. In some examples, theaudience detector 200 may have additional or alternative methods and/orcomponents to identify people in the frames captured by the image sensor204, such as a feedback system to which the members of the audience 106provide (e.g., actively and/or passively) identification to the audiencemeasurement device 104). To identify people in the frames captured bythe image sensor 204, the example person identifier 207 includes or hasaccess to a collection (e.g., stored in a database) of facial signatures(e.g., vectors). Each facial signature corresponds to a person having aknown identity to the person identifier 207. The collection includes anidentifier (ID) for each known facial signature that corresponds to aknown person. For example, in reference to FIG. 1, the collection offacial signatures may correspond to frequent visitors and/or members ofthe household associated with the room 100. The example personidentifier 207 analyzes one or more regions of a frame thought tocorrespond to a human face and develops a pattern or map for theregion(s). The pattern or map of the region represents a facialsignature of the detected human face. The example person identifier 207compares the detected facial signature to entries of the facialsignature collection. When a match is found, the person identifier 207has successfully identified at least one person in the frame. In suchinstances, the person identifier 207 records (e.g., in a memory addressdedicated to the person identifier 207) the ID associated with thematching facial signature of the collection. When a match is not found,the person identifier 207 of the illustrated example retries thecomparison or prompts the audience 106 for information that can be addedto the collection of known facial signatures for the unmatched face.More than one signature may correspond to the same face (i.e., the faceof the same person).

The example people counter 206 of FIG. 2 outputs the calculated talliesand/or corresponding image frames (or identifications thereof) to thetime stamper 208. Also, the person identifier 207 outputs the recordsID(s) for any identified persons and/or the corresponding image frames(or identifications thereof) to the time stamper 208. The time stamper208 of the illustrated example includes a clock and a calendar. Theexample time stamper 208 associates a time and date with each calculatedtally and/or ID and the corresponding frame by, for example, appendingthe time/date data to the end of the tally data, the ID(s), and/or theframe. A data package (e.g., the tally, the ID(s), the date and time,and/or the frame) is stored in the memory 210. The memory 210 mayinclude a volatile memory (e.g., Synchronous Dynamic Random AccessMemory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS DynamicRandom Access Memory (RDRAM, etc.) and/or a non-volatile memory (e.g.,flash memory). The memory 210 may include one or more double data rate(DDR) memories, such as DDR, DDR2, DDR3, mobile DDR (mDDR), etc. Thememory 210 may also include one or more mass storage devices such as,for example, hard drive disk(s), compact disk drive(s), digitalversatile disk drive(s), etc.

The example content identifier 202 of FIG. 2 includes a program detector212 and an output device 214. The example program detector 212 of FIG. 2detects presentation(s) of media content in the media exposureenvironment 100 and collects identification information associated withthe detected presentation(s). For example, the program detector 212,which may be in wired and/or wireless communication with thepresentation device 102 and/or the STB 108 of FIG. 1, can identify apresentation time and a source of a presentation. The presentation timeand the source identification data may be utilized to identify theprogram by, for example, cross-referencing a program guide configured,for example, as a look up table. The source identification data may, forexample, be the identity of a channel obtained, for example, bymonitoring a tuner of the STB 108 or a digital selection (e.g., a remotecontrol signal) of a channel to be presented on the television 102.Additionally or alternatively, codes embedded with or otherwisebroadcast with media content being presented via the STB 108 and/or thetelevision 102 may be utilized by the program detector 212 to identifythe presentation. As used herein, a code is an identifier that istransmitted with the media content for the purpose of identifying and/ortuning the corresponding media content. Codes may be carried in theaudio, in the video, in metadata, in a vertical blanking interval, in aprogram guide, in content data, or in any other portion of the mediacontent or the signal carrying the content. Additionally oralternatively, the program detector 212 can collect a signaturerepresentative of a portion of the media content. As used herein, asignature is a representation of some characteristic of the mediacontent (e.g., a frequency spectrum of an audio signal). Collectedsignature(s) can be compared against a collection of signatures of knownmedia content to identify the corresponding media content. Thesignature(s) can be collected by the program detector 212 and/or theprogram detector 212 can collect samples of the media content and exportthem to a remote site for generation of the signature(s). Irrespectiveof the manner in which the media content of the presentation isidentified, the identification information is time stamped by the timestamper 208 and may be stored in the memory 210.

In the illustrated example of FIG. 2, the output device 214 periodicallyand/or aperiodically exports the recorded data from the memory 214 to adata collection facility via a network (e.g., a local-area network, awide-area network, a metropolitan-area network, the Internet, a digitalsubscriber line (DSL) network, a cable network, a power line network, awireless communication network, a wireless mobile phone network, a Wi-Finetwork, etc.). The data collection facility of the illustrated exampleutilizes the people tallies generated by the people counter 206 and/orthe personal IDs generated by the person identifier 207 in conjunctionwith the content identifying data collected by the program detector 212to generate exposure information and/or compliance information (e.g.,indications of whether or not members of a panel have behaved inaccordance with term(s) of membership). Alternatively, the data analysiscould be performed locally and exported via a network or the like to adata collection facility for further processing. For example, the amountof people (as counted by the people counter 206) in the exposureenvironment 100 at a time (as indicated by the time stamp appended tothe people tally by the time stamper 208) in which a sporting event (asidentified by the program detector 212) was presented by the television102 can be used in a rating calculation for the sporting event. In someexamples, additional information (e.g., demographic data associated withone or more people personally identified by the person identifier 207,geographic data, etc.) is correlated with the exposure information atthe data collection facility to expand the usefulness of the datacollected by the example audience measurement device 104 of FIGS. 1and/or 2. The data collection facility of the illustrated examplecompiles data from many monitored exposure environments.

While an example manner of implementing the audience measurement device104 of FIG. 1 has been illustrated in FIG. 2, one or more of theelements, processes and/or devices illustrated in FIG. 2 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example audience detector 200, theexample content identifier 202, the example image sensor 204, theexample people counter 206, the person identifier 207, the example timestamper 208 and/or, more generally, the example audience measurementdevice 104 of FIG. 2 may be implemented by hardware, software, firmwareand/or any combination of hardware, software and/or firmware. Thus, forexample, any of the example audience detector 200, the example contentidentifier 202, the example image sensor 204, the example people counter206, the person identifier 207, the example time stamper 208 and/or,more generally, the example audience measurement device 104 of FIG. 2could be implemented by one or more circuit(s), programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)), field programmable gate array (FPGA), etc. When anyof the apparatus or system claims of this patent are read to cover apurely software and/or firmware implementation, at least one of theexample audience detector 200, the example content identifier 202, theexample image sensor 204, the example people counter 206, the personidentifier 207, the example time stamper 208, and/or the exampleaudience measurement device 104 of FIG. 2 are hereby expressly definedto include a tangible computer readable medium such as a memory, DVD,CD, BluRay, etc. storing the software and/or firmware. Further still,the example audience measurement device 104 of FIG. 2 may include one ormore elements, processes and/or devices in addition to, or instead of,those illustrated in FIG. 2, and/or may include more than one of any orall of the illustrated elements, processes and devices.

FIG. 3 is a block diagram of an example implementation of the exampleimage sensor 204 of FIG. 2. The example image sensor 204 of FIG. 3includes a resolution determiner 300, a high-resolution (hi-res) trigger302, a frame cache 303, an illumination controller 304, an illuminator306, an image capturer 308, a frame router 310, and a pixel binner 312.In the illustrated example, the image sensor 204 operates in a firstmode for frames not designated for processing that requireshigh-resolution image data. The first mode is referred to herein as amajority mode. In some examples, a default mode of the image sensor 204is the majority mode. The example image sensor 204 also operates in asecond mode for frames designated for processing that requireshigh-resolution image data, such as frames designated for a facialrecognition process to be executed by the example person identifier 207of FIG. 2. The second mode is referred to herein as a minority mode. Thefirst mode is referred to as the majority mode and the second mode isreferred to as a minority mode because the image sensor 204 is expected(but not necessarily) to be in the majority mode more often than theminority mode.

The resolution determiner 300 of the illustrated example determines inwhich mode the image sensor 204 is to operate for a given time period,an image frame and/or set of image frames. In the illustrated example ofFIG. 3, the resolution determiner 300 causes the image sensor to operatein the majority mode unless the minority mode has been triggered. Asdescribed above, the minority mode is to be entered when a frame to becaptured by the image capturer 308 is designated to undergo, forexample, a facial recognition process. To determine when an upcomingframe or set of frames is to undergo such resolution-demanding process,the example hi-res trigger 302 references the frame cache 303. Theexample frame cache 303 of FIG. 3 is a memory that stores framesrecently captured by the image capturer 308. The example frame cache 303of FIG. 3 receives frames directly from the image capturer 308 and/orthe pixel binner 312, which is described in detail below. The hi-restrigger 302 retrieves one or more of the recently captured frames fromthe frame cache 303 and analyzes the frame(s) to determine whether ashape corresponding to a human head and/or face appears in the previousframe(s). In some examples, the hi-res trigger 302 also determineswhether the detected head and/or face of the previous frames is orientedsuch that a facial recognition process can be successfully performed onthe corresponding image data. In some examples, the hi-res trigger 302also determines a period of time during which the detected head and/orface has been in a general location (e.g., if the corresponding personis not moving from a general position in the frame). Given the resultsof the one or more analyses performed by the hi-res trigger 302 on theprevious frame(s), the example hi-res trigger 302 of FIG. 3 determineswhether succeeding frame(s) are to undergo a facial recognition processto attempt to identify the detected head(s) and/or face(s). In someexamples, the hi-res trigger 302 is a clock that drives the system intothe minority mode at fixed intervals. For each frame that the hi-restrigger 302 designates as a frame that will be subject to a facialrecognition process (either via analysis or based on a clock), theexample hi-res trigger 302 of FIG. 3 places the image sensor 204 in theminority mode. Otherwise, the example image sensor 204 of FIG. 3operates according to the majority mode.

When the example image sensor 204 of FIG. 3 is operating in the majoritymode (e.g., the hi-res trigger 302 determines that facial recognition isnot to be performed on the corresponding frame(s)), the resolutiondeterminer 300 of the illustrated example conveys an instruction orindication to the illumination controller 304 that causes theillumination controller 304 to disable the illuminator for thecorresponding frame(s). In the illustrated example, the illuminator 306is an IR LED that emits IR light to illuminate the environment 100 withIR light detectable by the image capturer 308, which includes IRcapabilities. When in the majority mode, the example illuminationcontroller 304 coordinates with the image capturer 308 to synchronizeinaction of the illuminator 306 with the capture of the correspondingframe(s). Thus, the resolution determiner 300 causes the illuminator 306to be inactive during capture of frames not designated for facialrecognition processes (and/or only causes the illuminator 306 to beactive during capture of frames that are designated for facialrecognition processing. As described above, disabling the illuminator306 reduces power consumption and extends the lifetime of the imagesensor 204. Further, disabling the illuminator 306 reduces the heatgenerated by the illuminator and, thus, reduces or even eliminates theneed for heat sinking devices and/or techniques. Further, when theilluminator 306 is not blinking and/or glowing (e.g., when active),members of the audience 106 are less likely to become annoyed by theimage sensor 204.

When the example image sensor 204 of FIG. 3 is operating in the minoritymode (e.g., the hi-res trigger 302 determines that facial recognition isto be performed on the corresponding frame(s)), the resolutiondeterminer 300 conveys an instruction to the illumination controller 304that causes the illumination controller 304 to enable the illuminator306 during capture of the corresponding frame(s). The exampleillumination controller 304 coordinates with the image capturer 308 tosynchronize operation of the illuminator 306 with the capture of thecorresponding frame(s). Thus, the hi-res trigger 302 causes theilluminator 306 to be active for frames designated for facialrecognition processes (e.g., by the person identifier 207). Frames maybe specifically designated or may be generally designated as “frameswhich occur at a certain time.” Accordingly, when operating in themajority mode, the illuminator 306 does not illuminate the environment100. In contrast, when operating the minority mode, the illuminator 306does illuminate the environment 100.

The example image capturer 308 of FIG. 3 is a high-resolution camerathat captures image data representative of the environment 100 ofFIG. 1. In the illustrated example, the image capturer 308 operatessimilarly in the majority mode as in the minority mode. That is, theexample image capturer 308 of FIG. 3 captures high-resolution image datafor each captured frame. Given the selective operation of theilluminator 306 described above, the high-resolution frames capturedwhen operating in the majority mode are likely to be poorly lit. On theother hand, the high-resolution frames captured when operating in theminority mode are well lit. The example image capturer 308 of FIG. 3conveys all or some (e.g., only the frames captured in the minoritymode) of the captured frames to the resolution determiner 300 forstorage in the frame cache 303. As described above, the frames stored inthe frame cache 303 are used by the example hi-res trigger 302 toidentify heads and/or faces in the environment 100 that, due to theirorientation, may be identifiable by the person identifier 207.

Further, the example image capturer 308 of FIG. 3 conveys the capturedhigh-resolution frames to the frame router 310. The example frame router310 of FIG. 3 also receives an instruction from the resolutiondeterminer 300 indicative of the mode (e.g., majority or minority) inwhich the image sensor 204 is operating for the received frames. Forframes designated by the resolution determiner 300 as frames captured inthe minority mode, the illuminator 306 was active and, thus, the imagedata is of high-resolution and well lit. Accordingly, the image datacaptured in the minority mode is in condition for the facial recognitionprocess. Therefore, in response to the indicator from the resolutiondeterminer 300 that the received frame(s) were captured in the minoritymode, the example frame router 310 of FIG. 3 routes the illuminated,high-resolution frames to the person identifier 207 for facialrecognition process(es). In some examples, the frame router 310 alsoroutes the high-resolution frames to the people counter 206 for countingprocess(es).

For frames designated by the resolution determiner 300 as framescaptured in the majority mode, the illuminator 306 was inactive and,thus, the image data is of high-resolution but likely to be dark and oflow contrast. For many of these frames, the contrast level is likely tobe too low for proper analysis (e.g., people counting) to be accuratelyperformed. Accordingly, the image data captured in the majority mode maynot be in condition (e.g., may not have high enough contrast levels) foran accurate execution of the people counting process of the peoplecounter 206. Therefore, in response to the instruction from theresolution determiner 300 that the received frame(s) were captured inthe majority mode, the example frame router 310 of FIG. 3 routes thelow-contrast frames to the pixel binner 312.

The example pixel binner 312 of FIG. 3 receives high-resolution framesof image data from the frame router 310 that are likely to be of lowcontrast levels due to the illuminator 306 being inactive during captureof the frames. The example pixel binner 312 of FIG. 3 performs a binningprocedure on the received frames. Binning is a process in which aneighborhood of pixels (e.g., a first pixel and a number of adjacentpixels) is summed together. Example binning procedures sum pixelsaccording to a 2×2 neighborhood of pixels, a 3×3 neighborhood of pixels,a 4×4 neighborhood of pixels, or another arrangement of pixels. Ineffect, the binning procedure performed by the pixel binner 312 mayassign more light per orthogonal block of pixels at the cost of lowerresolution. The additional light per block pixel increases the contrastlevels of the image data. After the pixel procedure performed by theexample pixel binner 312 of FIG. 3, the frames are of lower resolution(e.g., in comparison with the original format of the frame as capturedby the high-resolution camera of the image capturer 308) but may alsohave higher contrast levels. Thus, the example pixel binner 312 enhancesthe dark, low-contrast images generated in the majority mode forprocessing that does not require high-resolution image data (e.g., thefacial recognition process of the person identifier 207), such as thepeople counting process of the people counter 206. The example pixelbinner 312 conveys the resulting frames of image data to the peoplecounter 206. The higher contrast levels provided by the example pixelbinner 312 may enable the people counter 206 to distinguish betweenshapes in the frames of image data. The example pixel binner 312 of FIG.3 also conveys the frames of image data to the resolution determiner 300for storage in the frame cache 303.

While an example manner of implementing the image sensor 204 of FIG. 2has been illustrated in FIG. 3, one or more of the elements, processesand/or devices illustrated in FIG. 3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example resolution determiner 300, the example hi-restrigger 302, the example illumination controller 304, the example framerouter 310, the example pixel binner 310, and/or, more generally, theexample image sensor 204 of FIG. 3 may be implemented by hardware,software, firmware and/or any combination of hardware, software and/orfirmware. Thus, for example, any of the example resolution determiner300, the example hi-res trigger 302, the example illumination controller304, the example frame router 310, the example pixel binner 310, and/or,more generally, the example image sensor 204 of FIG. 3 could beimplemented by one or more circuit(s), programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)),field programmable gate array (FPGA), etc. When any of the apparatus orsystem claims of this patent are read to cover a purely software and/orfirmware implementation, at least one of, the example resolutiondeterminer 300, the example hi-res trigger 302, the example illuminationcontroller 304, the example frame router 310, the example pixel binner310, and/or the example image sensor 204 of FIG. 3 are hereby expresslydefined to include a tangible computer readable medium such as a memory,DVD, CD, Bluray, etc. storing the software and/or firmware. Furtherstill, the example image sensor 204 of FIG. 3 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 3, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

FIG. 4 is a flowchart representative of example machine readableinstructions for implementing the example image sensor 204 of FIGS. 1, 2and/or 3. In this example, the machine readable instructions comprise aprogram for execution by a processor such as the processor 512 shown inthe example processing system 500 discussed below in connection withFIG. 5. The program may be embodied in software stored on a tangiblecomputer readable medium such as a CD-ROM, a floppy disk, a hard drive,a digital versatile disk (DVD), a BluRay disk, or a memory associatedwith the processor 512, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor 512and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIG. 4, many other methods of implementing the example image sensor204 may alternatively be used. For example, the order of execution ofthe blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined.

As mentioned above, the example processes of FIG. 4 may be implementedusing coded instructions (e.g., computer readable instructions) storedon a tangible computer readable medium such as a hard disk drive, aflash memory, a read-only memory (ROM), a compact disk (CD), a digitalversatile disk (DVD), a cache, a random-access memory (RAM) and/or anyother storage media in which information is stored for any duration(e.g., for extended time periods, permanently, brief instances, fortemporarily buffering, and/or for caching of the information). As usedherein, the term tangible computer readable medium is expressly definedto include any type of computer readable storage and to excludepropagating signals. Additionally or alternatively, the exampleprocesses of FIG. 4 may be implemented using coded instructions (e.g.,computer readable instructions) stored on a non-transitory computerreadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage media in which informationis stored for any duration (e.g., for extended time periods,permanently, brief instances, for temporarily buffering, and/or forcaching of the information). As used herein, the term non-transitorycomputer readable medium is expressly defined to include any type ofcomputer readable medium and to exclude propagating signals. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended. Thus, a claim using “at least” as thetransition term in its preamble may include elements in addition tothose expressly recited in the claim.

FIG. 4 begins with an initiation of the example image sensor 204 of FIG.3 (block 400). In the illustrated example, the initiation of the imagesensor 204 corresponds to an initiation of a monitoring session of theexample exposure environment 100 of FIG. 1. The example resolutiondeterminer 300 determines whether the next frame(s) to be captured areto be subjected to processing that demands high-resolution image data(block 402). In the illustrated example, the amount of frames for whichthe determination is to be made varies depending on, for example, anadjustable setting and/or a finding of the hi-res trigger 302 regardingprevious frame(s). As described above, the determination of theresolution determiner 300 indicates which mode (e.g., majority (e.g.,low resolution) mode or minority (e.g., high resolution) mode) in whichthe image sensor 204 is to operate for the next frame(s). In theillustrated example, the resolution determiner 300 bases thedetermination on whether the next frame(s) to be captured will undergo afacial recognition process implemented by the example person identifier207 of FIG. 2. For example, the hi-res trigger 302 of FIG. 3 determineswhether a head or face is included in previous frame(s) (e.g., frames(s)obtained from the frame cache 303) capable of providing anidentification of a corresponding person.

In the example of FIG. 4, when the resolution determiner 300 determinesthat the next frame(s) to be captured are not going to be subjected tothe facial recognition process (block 402), the example resolutiondeterminer 300 provides an instruction to the illumination controller304 to disable the illuminator 306 during capture of the next frame(s)(block 404). In such instances, the illumination controller 304cooperates with the image capturer 308 to capture the next frame(s) withthe illuminator 306 deactivated (block 406). While the resulting framesare captured with the high-resolution camera of the image capturer 308,the frames are not lit by the illuminator 306 and, thus, likely to bedark and to have low contrast levels. To increase the contrast levels ofthe frame(s) captured without use of the illuminator 306, the framerouter 310 routes the frame(s) to the pixel binner 312 (block 408). Theexample frame router 310 of FIG. 3 is aware of which frames werecaptured without use of the illuminator 306 via the instruction providedthereto by the resolution determiner 300 regarding in which mode (e.g.,majority mode or minority mode) the image sensor 204 is operating.

The example pixel binner 312 of FIG. 3 performs a binning procedure(e.g., a 2×2 binning) on the received frame(s) (block 410). As describedabove, the binning procedure increases the contrast levels of the imagedata at the expense of the resolution of the image data. The lowerresolution is acceptable for the frame(s) captured while in the majoritymode because the people counting process(es) for which the majority modeframes are designated does not require high-resolution data, butoperates more accurately with higher contrast levels. Having performedthe binning procedure on the majority mode frames, the example pixelbinner 312 conveys the binned frame(s) to the people counter 206 and theframe cache 303 (block 412). Control returns to block 402.

Referring back to block 402, when the resolution determiner 300determines that the next frame(s) to be captured are going to besubjected to the facial recognition process, the example resolutiondeterminer 300 provides an instruction to the illumination controller304 to enable the illuminator 306 during capture of the next frame(s)(block 414). In such instances, the illumination controller 304cooperates with the image capturer 308 to capture the next frame(s) withthe illuminator 306 activated (e.g., emitting light, such as IR lightinto the exposure environment 100 of FIG. 1) (block 416). The resultingframes are captured with the high-resolution camera of the imagecapturer 308 and the frames are lit by the illuminator 306. Thus, theminority mode frames are ready for processing by the person identifier207. Accordingly, the route framer 310 conveys the minority mode framesto the person identifier 207 and the frame cache 303 (block 418).Control returns to block 402.

While the example image sensor of FIGS. 1, 2 and/or 3 is described inthe context of an audience measurement device 104 and the generation ofexposure data for media content, the example methods, apparatus, andarticles of manufacture disclosed herein can be applied to additional oralternative contexts, systems, measurements, applications, programs,etc. That is, the example methods, apparatus, and articles ofmanufacture disclosed herein can be used in any application toselectively operate an illumination source of an image sensor based on aresolution demand for the resulting frames of image data.

FIG. 5 is a block diagram of an example processor system 510 capable ofexecuting the instructions of FIG. 4 to implement the example imagesensor 204 of FIGS. 2 and/or 3. The processor system 510 can be, forexample, a server, a personal computer, a mobile phone, a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a BluRay player, a gaming console, apersonal video recorder, a set-top box, an audience measurement device,or any other type of computing device.

The example processor system 510 of FIG. 5 includes a processor 512 thatis coupled to an interconnection bus 514. The processor 512 may be anysuitable processor, processing unit, or microprocessor (e.g., one ormore Intel® microprocessors from the Pentium® family, the Itanium®family or the XScale® family and/or other processors from otherfamilies). The system 510 may be a multi-processor system and, thus, mayinclude one or more additional processors that are identical or similarto the processor 512 and that are communicatively coupled to theinterconnection bus 514.

The processor 512 of FIG. 5 is coupled to a chipset 518, which includesa memory controller 520 and an input/output (I/O) controller 522. Achipset provides I/O and memory management functions as well as aplurality of general purpose and/or special purpose registers, timers,etc. that are accessible or used by one or more processors coupled tothe chipset 518. The memory controller 520 performs functions thatenable the processor 512 to access a system memory 524, a mass storagememory 525, and/or a digital versatile disk (DVD) 540.

In general, the system memory 524 may include any desired type ofvolatile and/or non-volatile memory such as, for example, static randomaccess memory (SRAM), dynamic random access memory (DRAM), flash memory,read-only memory (ROM), double data rate memory (DDR), etc. The massstorage memory 525 may include any desired type of mass storage deviceincluding hard disk drives, optical drives, tape storage devices, etc.The machine readable instructions of FIGS. 5A-5C may be stored in thesystem memory 524, the mass storage memory 525, and/or the DVD 540.

The I/O controller 522 performs functions that enable the processor 512to communicate with peripheral input/output (I/O) devices 526 and 528and a network interface 530 via an I/O bus 532. The I/O devices 526 and528 may be any desired type of I/O device such as, for example, akeyboard, a video display or monitor, a mouse, etc. The networkinterface 530 may be, for example, an Ethernet device, an asynchronoustransfer mode (ATM) device, an 802.11 device, a digital subscriber line(DSL) modem, a cable modem, a cellular modem, etc. that enables theprocessor system 510 to communicate with another processor system. Theexample network interface 530 of FIG. 5 is also communicatively coupledto a network 534, such as an intranet, a Local Area Network, a Wide AreaNetwork, the Internet, etc.

While the memory controller 520 and the I/O controller 522 are depictedin FIG. 5 as separate functional blocks within the chipset 518, thefunctions performed by these blocks may be integrated within a singlesemiconductor circuit or may be implemented using two or more separateintegrated circuits.

Although certain example apparatus, methods, and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all apparatus,methods, and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A mobile phone comprising: an image sensor tocapture a first image of a face; and a logic circuit to: determinewhether the face in the first image is in a first orientation withrespect to the image sensor; and when the face in the first image is inthe first orientation: trigger the image sensor to capture a secondimage; and perform a facial recognition process on the second image, butnot on the first image.
 2. The mobile phone of claim 1, furtherincluding an illumination source, wherein the second image is capturedwhile the illumination source is used to illuminate the face.
 3. Themobile phone of claim 1, wherein the image sensor is to operate in afirst mode to capture the first image at a first resolution, the imagesensor is to operate in a second mode to capture the second image at asecond resolution, the second resolution higher than the firstresolution, and the facial recognition process is performed on imagedata of the second resolution included in the second image.
 4. Themobile phone of claim 1, wherein the logic circuit is further to: whenthe face in the first image is not in the first orientation: trigger theimage sensor to capture the second image; determine whether the secondimage includes the face in the first orientation with respect to theimage sensor; and not perform the facial recognition process on thesecond image when the face in the second image is not in the firstorientation.
 5. The mobile phone of claim 2, wherein the illuminationsource is at least one of a flash or a display.
 6. The mobile phone ofclaim 5, wherein the first image is captured without the flash emittinglight.
 7. The mobile phone as defined in claim 1, further including abinner to bin pixels of the first image.
 8. A tangible computer readablemedium comprising instructions that, when executed, cause a mobile phoneto at least: capture a first image of a face; and determine whether theface in the first image is in a first orientation with respect to animage sensor; and when the face in the first image is in the firstorientation: capture a second image; and perform a facial recognitionprocess on the second image, but not on the first image.
 9. The computerreadable medium as defined in claim 8, wherein the second image iscaptured while an illumination source is illuminated.
 10. The computerreadable medium as defined in claim 8, wherein the mobile phone is tooperate in a first mode to capture the first image at a firstresolution, the mobile phone is to operate in a second mode to capturethe second image at a second resolution, the second resolution higherthan the first resolution, and the facial recognition process isperformed on image data of the second resolution included in the secondimage.
 11. The computer readable medium as defined in claim 8, whereinthe instruction, when executed, further cause the mobile phone to: whenthe face in the first image is not in the first orientation: trigger theimage sensor to capture the second image; determine whether the secondimage includes the face in the first orientation with respect to theimage sensor; and not perform the facial recognition process on thesecond image when the face in the second image is not in the firstorientation.
 12. The computer readable medium as defined in claim 9,wherein the illumination source is at least one of a flash or a display.13. The computer readable medium as defined in claim 12, wherein thefirst image is captured without the flash emitting light.
 14. Thecomputer readable medium as defined in claim 8, further including abinner to bin pixels of the first image.
 15. A mobile phone comprising:image capturing means for capturing a first image of a face; orientationdetermining means for determining whether the face in the first image isin a first orientation with respect to the image capturing means;triggering means for triggering the image capturing means to capture asecond image when the face in the first image is in the firstorientation; and facial recognition means for performing a facialrecognition process on the second image, but not on the first image,when the face in the first image is in the first orientation.
 16. Themobile phone of claim 15, further including illumination means, whereinthe second image is captured while the illumination means is used toilluminate the face.
 17. The mobile phone of claim 15, wherein the imagecapturing means is to operate in a first mode to capture the first imageat a first resolution, the image capturing means is to operate in asecond mode to capture the second image at a second resolution, thesecond resolution higher than the first resolution, and the facialrecognition process is performed on image data of the second resolutionincluded in the second image.
 18. The mobile phone of claim 15, wherein:the triggering means is to trigger the image capturing means to capturethe second image when the face in the first image is not in the firstorientation; and the orientation determining means is to determinewhether the face in the second image is in the first orientation withrespect to the image capturing means; and the facial recognition meansis not to perform the facial recognition process on the second imagewhen the face in the second image is not in the first orientation. 19.The mobile phone of claim 16, wherein the illumination means is at leastone of a flash or a display.
 20. The mobile phone of claim 19, whereinthe first image is captured without the flash emitting light.